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We are seeking a hands-on Director of Tech Ops to own and evolve our backend platforms, data infrastructure, DevOps, security, and internal IT operations.
This role is foundational to our ability to securely build, train, validate, and operate state-of-the-art autonomous driving systems at scale. The ideal candidate brings deep experience in operating secure, production-grade infrastructure in a data-intensive AI environment, while also leading and growing high-performing teams.
Responsibilities
Own backend infrastructure, DevOps, security, and internal IT operations end-to-end
Design, build, and operate scalable cloud and on-prem infrastructure supporting large-scale AI training, validation, and data pipelines
Lead the architecture and operation of systems handling massive volumes of visual and multimodal data
Establish and continuously improve DevOps practices, including CI/CD, monitoring, alerting, reliability, security, and cost optimization
Define, implement, and enforce infrastructure and data security standards, policies, and controls
Lead security compliance efforts, including implementation and successful audits of frameworks such as ISO 27001 and similar standards
Manage corporate IT systems, access control, networking, endpoints, and external vendors
Partner closely with AI, algorithm, and R&D teams to support ML workflows and evolving compute and data needs
Drive selection, adoption, and integration of cloud platforms, databases, DevOps tools, and third-party services
Define best practices for scalability, availability, resilience, and security across the organization
Lead, mentor, and scale backend, DevOps, security, and IT teams.

Required Deep Learning Algorithm Developer - ADAS group
As part of the algorithm team for the next generation of autonomous driving, you will:
Responsibilities
Understand deeply, implement, train, and evaluate recent state-of-the-art algorithms in computer vision (e.g., object detection, semantic segmentation, self-supervised learning, vision transformers) for various domains.
Adapt algorithms into robust and scalable production-ready algorithms. Write clean and efficient code, ensuring compatibility with existing systems and frameworks, and real-time needs. Take full responsibility for your algorithms end-to-end until deployment on the edge device for production.
Develop and implement new computer vision algorithms (architectures, loss functions, post-processing, training, and evaluation methods) that surpass SOTA algorithms and achieve desired performance levels in our domain.

We are seeking a hands-on Applied AI Scientist to join our core R&D team and drive the development of next-generation AI systems for autonomous driving. This role sits at the intersection of applied research and deployment - you will go from reading papers to shipping production systems. You will work directly on our multi-layered autonomy architecture, with a primary focus on real-time predictive models for driving decisions.
A deep technical role for someone who thrives on turning cutting-edge research into real, working systems under hard constraints.
Responsibilities:
Own the research-to-deployment cycle for predictive driving models - from literature review and prototyping through to production integration
Design, implement, and iterate on real-time predictive models, including vision-language models, motion prediction models, and inverse reinforcement learning approaches (e.g., imitation learning, reward recovery)
Collaborate on higher-level reasoning systems, contributing to vision-language-action models that handle complex edge cases and long-horizon planning
Bridge cloud-scale training with edge deployment - work on model compression, quantization, speculative decoding, and efficient inference for embedded automotive platforms
Evaluate and integrate state-of-the-art techniques from the broader AI research community into our autonomy stack
Collaborate closely with internal R&D teams to unblock technical challenges, accelerate delivery, and raise the overall technical bar.